{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "xl-detector_colab_v1.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.11"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rBX6UgKeR8Dg"
      },
      "source": [
        "# K210【目标检测】模型的训练"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vBDQ4Y_WR8Dk"
      },
      "source": [
        "# "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3VS7nd6IR8Dm"
      },
      "source": [
        "## 设置 colab硬盘绑定，方便循环使用"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ViBCCJ2qR8Dm",
        "outputId": "3d07d909-5cfb-4941-859f-dd61cec43e59"
      },
      "source": [
        "# https://blog.csdn.net/aiynmimi/article/details/88238246\n",
        "# ##这里的输入框中，填入授权的code，回车 ##\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8OrEuYa7R8Dn"
      },
      "source": [
        "# set colab\n",
        "import os\n",
        "os.chdir(\"/content/drive/My Drive/Colab Notebooks\")\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_-y_M2q7R8Do"
      },
      "source": [
        "##  获取git代码构建环境"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zRWAyAymR8Do",
        "outputId": "d1cd85ff-5e9a-4a58-c748-a369bdc846cf"
      },
      "source": [
        "!ls ./"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data  models  share  train  ts_save\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SRC53sD8UHtr",
        "outputId": "3fdeec8b-5549-4e61-9de4-c2debc8e91d8"
      },
      "source": [
        "!pwd"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Colab Notebooks\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2KdI3KlmR8Dp",
        "outputId": "e4e9b733-fa9c-4cdc-b1b4-194130d9741d"
      },
      "source": [
        "!mkdir ./train\n",
        "!mkdir ./train/k210_work\n",
        "!mkdir ./train/k210_work/external-libraries\n",
        "\n",
        "work_dir = './train/k210_work' # 工作目录\n",
        "git_dir = f'{work_dir}/k210' # git代码目录"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mkdir: cannot create directory ‘./train’: File exists\n",
            "mkdir: cannot create directory ‘./train/k210_work’: File exists\n",
            "mkdir: cannot create directory ‘./train/k210_work/external-libraries’: File exists\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T6B0X-KwR8Dq",
        "jupyter": {
          "outputs_hidden": false
        }
      },
      "source": [
        "# %%bash\n",
        "# if [ -d \"./train/k210_work/k210\" ]; then\n",
        "#   rm -f -r \"./train/k210_work/k210\"\n",
        "# fi\n",
        "# # git代码没更新，就没必要删除重新获取"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TzYF5_FxR8Dr",
        "jupyter": {
          "outputs_hidden": false
        },
        "outputId": "bdd03e19-79ac-442e-f961-3bbbb5ec47f3"
      },
      "source": [
        "# %%bash\n",
        "# cd ./train/k210_work\n",
        "# ##git clone https://gitee.com/ai-liam/k210\n",
        "# git clone https://github.com/ai-liam/k210\n",
        "## 下载过就不必要下载"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Cloning into 'k210'...\n",
            "Checking out files: 100% (154/154), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p2O5P5gyR8Dt",
        "jupyter": {
          "outputs_hidden": false
        }
      },
      "source": [
        "## 安装过就必要在安装\n",
        "#!pip install -r ./train/k210_work/k210/k210_train/requirements.txt -t ./train/k210_work/external-libraries\n",
        "##!pip install -r ./train/k210_work/k210/k210_train/requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ -t /home/aistudio/external-libraries"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qzmhmpD_R8Dt"
      },
      "source": [
        "## 上传和设置数据源目录"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gh3fdI0xR8Du",
        "jupyter": {
          "outputs_hidden": false
        }
      },
      "source": [
        "# 把你的 xx.zip 数据上到 work目录，【注意，解压得到的文件名】参考例子数据\n",
        "upload_zip = f\"{git_dir}/datasets/detector_xml_format.zip\"# demo数据\n",
        "data_unzip = f\"{git_dir}/datasets/ts_xml_format/\"\n",
        "data_unzip_last = data_unzip+\"xml_format/\" #最后解压后的目录"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xuswQSjpXhrv",
        "outputId": "81a3636f-888b-4920-fc98-e5c96537a1b3"
      },
      "source": [
        "## 解压过就不用再解压\n",
        "import os\n",
        "\n",
        "cmd = f\"unzip -o -d {data_unzip} {upload_zip}\"\n",
        "f=os.popen(cmd)\n",
        "print(f.read())"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  ./train/k210_work/k210/datasets/detector_xml_format.zip\n",
            "   creating: ./train/k210_work/k210/datasets/ts_xml_format/xml_format/\n",
            "   creating: ./train/k210_work/k210/datasets/ts_xml_format/xml_format/images/\n",
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            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E8KrdDuWR8Du"
      },
      "source": [
        "## 开始训练"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Esqo26jYR8Du",
        "jupyter": {
          "outputs_hidden": false
        },
        "outputId": "54345aa6-784b-41a1-94e9-3b27d337fd3a"
      },
      "source": [
        "import sys \n",
        "sys.path.append(f'{work_dir}/external-libraries')\n",
        "sys.path.append(f'{git_dir}/k210_train')\n",
        "\n",
        "!ls"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data  models  share  train  ts_save\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NNWmx9VDR8Dv",
        "jupyter": {
          "outputs_hidden": false
        }
      },
      "source": [
        "import os, sys\n",
        "import time\n"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "at81wJUBR8Dw",
        "jupyter": {
          "outputs_hidden": false
        }
      },
      "source": [
        "from train.detector_v1 import detector_train\n",
        "from train.detector.instance import config\n",
        "\n",
        "config.detector_train_epochs = 1 # 修改训练轮数 40"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y1M0XIudR8Dw",
        "jupyter": {
          "outputs_hidden": false
        },
        "outputId": "573fd9a8-4d5e-420c-80a9-8c7ab5d64aa3"
      },
      "source": [
        "cur_time = time.strftime(\"%Y-%m-%d_%H_%M\", time.localtime())\n",
        "print(cur_time)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2021-09-27_06_10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HgflF84KR8Dx",
        "jupyter": {
          "outputs_hidden": false
        },
        "outputId": "10fee1df-3782-46d9-9049-8b2c9a4bbaf3"
      },
      "source": [
        "#root_dir = './train/k210_work/k210' # os.path.abspath('./train/k210_work')\n",
        "ubuntu_path = f\"out/yolo_{cur_time}\" #用来mac中生成kmodel\n",
        "temp_saves_dir = f\"{git_dir}/ubuntu/{ubuntu_path}/\" #保存文件目录\n",
        "datasets_dir =  data_unzip_last #数据源\n",
        "\n",
        "print(\"git_dir:\",git_dir)\n",
        "print(\"temp_saves_dir:\",temp_saves_dir)\n",
        "print(\"datasets_dir:\",datasets_dir)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "git_dir: ./train/k210_work/k210\n",
            "temp_saves_dir: ./train/k210_work/k210/ubuntu/out/yolo_2021-09-27_06_10/\n",
            "datasets_dir: ./train/k210_work/k210/datasets/ts_xml_format/xml_format/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UvJ4y0U8aWiC",
        "outputId": "4229cb0b-4805-4d88-ccc9-1432facab517"
      },
      "source": [
        "## 清理隐藏文件\n",
        "cmd1 = f\"rm -f {datasets_dir}/.DS_Store\"\n",
        "os.popen(cmd1)\n",
        "cmd = f\"rm -f {datasets_dir}/*/.DS_Store\"\n",
        "f = os.popen(cmd)\n",
        "print(f)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<os._wrap_close object at 0x7f518e4f95d0>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6eKSaTxXR8Dx",
        "jupyter": {
          "outputs_hidden": false
        },
        "outputId": "df5204ed-163e-4b0e-acda-8169c56b9f16"
      },
      "source": [
        "# 配置文件：train.detector.instance\n",
        "result,_,model_path,anchors,labels = detector_train(datasets_dir=datasets_dir,temp_datasets_dir=temp_saves_dir,config=config)\n"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "select gpu fail:Driver Not Loaded\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2021-09-27 06:10:50,874 - [INFO]:  no GPU, will use [CPU]\n",
            "2021-09-27 06:10:52,870 - [INFO]:  input_shape: (224, 224, 3)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-- 0\n",
            "-- 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2021-09-27 06:10:55,150 - [INFO]:  load datasets complete, check pass, images num:600, bboxes num:600\n",
            "2021-09-27 06:11:00,291 - [INFO]:  train, labels:['0', '1']\n",
            "2021-09-27 06:11:00,299 - [DEBUG]:  train, datasets dir:./train/k210_work/k210/datasets/ts_xml_format/xml_format/\n",
            "2021-09-27 06:11:00,305 - [INFO]:  bboxes num: 600, first bbox: [0.59821429 0.625     ]\n",
            "2021-09-27 06:11:00,416 - [INFO]:  bbox accuracy(IOU): 85.43%\n",
            "2021-09-27 06:11:00,418 - [INFO]:  bound boxes: (136.000000,131.00),(78.000000,77.00),(165.000000,165.00),(54.000000,57.00),(105.000000,105.00)\n",
            "2021-09-27 06:11:00,421 - [INFO]:  anchors: [4.25, 4.09375, 2.4375, 2.40625, 5.15625, 5.15625, 1.6875, 1.78125, 3.28125, 3.28125]\n",
            "2021-09-27 06:11:00,423 - [INFO]:  w/h ratios: [0.95, 1.0, 1.0, 1.01, 1.04]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "self.anchors: [4.25, 4.09375, 2.4375, 2.40625, 5.15625, 5.15625, 1.6875, 1.78125, 3.28125, 3.28125]\n",
            "load local weight file: /content/drive/My Drive/Colab Notebooks/train/k210_work/k210/k210_train/train/detector/weights/mobilenet_7_5_224_tf_no_top.h5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/optimizer_v2.py:356: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  \"The `lr` argument is deprecated, use `learning_rate` instead.\")\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:1972: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  warnings.warn('`Model.fit_generator` is deprecated and '\n",
            "2021-09-27 06:11:05,496 - [INFO]:  train start\n",
            "2021-09-27 06:11:05,501 - [INFO]:  progress: 1.0%, train start\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
            "_________________________________________________________________\n",
            "mobilenet_0.75_224 (Function (None, 7, 7, 768)         1832976   \n",
            "=================================================================\n",
            "Total params: 1,832,976\n",
            "Trainable params: 1,816,560\n",
            "Non-trainable params: 16,416\n",
            "_________________________________________________________________\n",
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
            "_________________________________________________________________\n",
            "mobilenet_0.75_224 (Function (None, 7, 7, 768)         1832976   \n",
            "_________________________________________________________________\n",
            "detection_layer_35 (Conv2D)  (None, 7, 7, 35)          26915     \n",
            "_________________________________________________________________\n",
            "reshape (Reshape)            (None, 7, 7, 5, 7)        0         \n",
            "=================================================================\n",
            "Total params: 1,859,891\n",
            "Trainable params: 1,843,475\n",
            "Non-trainable params: 16,416\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2021-09-27 06:11:05,570 - [INFO]:  epoch 0 start\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "480/480 [==============================] - 318s 652ms/step - loss: 0.3290 - val_loss: 0.2100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2021-09-27 06:16:23,629 - [INFO]:  epoch 0 end: {'loss': 0.3290134370326996, 'val_loss': 0.20997080206871033, 'lr': 1e-04}\n",
            "2021-09-27 06:16:23,632 - [INFO]:  progress: 98.0%, train epoch end\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.20997, saving model to ./train/k210_work/k210/ubuntu/out/yolo_2021-09-27_06_10/m_best.h5\n",
            "save .h5 weights file to : ./train/k210_work/k210/ubuntu/out/yolo_2021-09-27_06_10/m_best.h5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2021-09-27 06:16:23,847 - [INFO]:  train end\n",
            "2021-09-27 06:16:23,856 - [INFO]:  progress: 98.0%, train end\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5-mins to train\n",
            "save .h5 weights file to : ./train/k210_work/k210/ubuntu/out/yolo_2021-09-27_06_10/m.h5\n",
            "save tfilte to : ./train/k210_work/k210/ubuntu/out/yolo_2021-09-27_06_10/m.tflite\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/keras/layers/normalization/batch_normalization.py:520: _colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/lite/python/util.py:300: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.convert_variables_to_constants`\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/convert_to_constants.py:927: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.compat.v1.graph_util.extract_sub_graph`\n",
            "INFO:tensorflow:Assets written to: /tmp/tmpwzp6ogu7/assets\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/lite/python/convert_saved_model.py:63: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.loader.load or tf.compat.v1.saved_model.load. There will be a new function for importing SavedModels in Tensorflow 2.0.\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpwzp6ogu7/variables/variables\n",
            "INFO:tensorflow:The given SavedModel MetaGraphDef contains SignatureDefs with the following keys: {'serving_default', '__saved_model_init_op'}\n",
            "INFO:tensorflow:input tensors info: \n",
            "INFO:tensorflow:Tensor's key in saved_model's tensor_map: input_1\n",
            "INFO:tensorflow: tensor name: serving_default_input_1:0, shape: (-1, 224, 224, 3), type: DT_FLOAT\n",
            "INFO:tensorflow:output tensors info: \n",
            "INFO:tensorflow:Tensor's key in saved_model's tensor_map: reshape\n",
            "INFO:tensorflow: tensor name: StatefulPartitionedCall:0, shape: (-1, 7, 7, 5, 7), type: DT_FLOAT\n",
            "INFO:tensorflow:Restoring parameters from /tmp/tmpwzp6ogu7/variables/variables\n",
            "anchors 2: [4.25, 4.09375, 2.4375, 2.40625, 5.15625, 5.15625, 1.6875, 1.78125, 3.28125, 3.28125]\n",
            "save tfilte to : ./train/k210_work/k210/ubuntu/out/yolo_2021-09-27_06_10/m.tflite\n",
            "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: /tmp/tmpuvuuxlzc/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Assets written to: /tmp/tmpuvuuxlzc/assets\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Restoring parameters from /tmp/tmpuvuuxlzc/variables/variables\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Restoring parameters from /tmp/tmpuvuuxlzc/variables/variables\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:The given SavedModel MetaGraphDef contains SignatureDefs with the following keys: {'serving_default', '__saved_model_init_op'}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:The given SavedModel MetaGraphDef contains SignatureDefs with the following keys: {'serving_default', '__saved_model_init_op'}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:input tensors info: \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:input tensors info: \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Tensor's key in saved_model's tensor_map: input_1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Tensor's key in saved_model's tensor_map: input_1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow: tensor name: serving_default_input_1:0, shape: (-1, 224, 224, 3), type: DT_FLOAT\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow: tensor name: serving_default_input_1:0, shape: (-1, 224, 224, 3), type: DT_FLOAT\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:output tensors info: \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:output tensors info: \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Tensor's key in saved_model's tensor_map: reshape\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Tensor's key in saved_model's tensor_map: reshape\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow: tensor name: StatefulPartitionedCall:0, shape: (-1, 7, 7, 5, 7), type: DT_FLOAT\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow: tensor name: StatefulPartitionedCall:0, shape: (-1, 7, 7, 5, 7), type: DT_FLOAT\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Restoring parameters from /tmp/tmpuvuuxlzc/variables/variables\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:tensorflow:Restoring parameters from /tmp/tmpuvuuxlzc/variables/variables\n",
            "2021-09-27 06:17:45,817 - [INFO]:  train ok, now generate report\n",
            "INFO:logger: train ok, now generate report\n",
            "2021-09-27 06:17:45,821 - [INFO]:  generate report image\n",
            "INFO:logger: generate report image\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<keras.callbacks.History object at 0x7f5133114fd0>\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2021-09-27 06:17:46,091 - [INFO]:  generate report image end\n",
            "INFO:logger: generate report image end\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qH5kq63eR8Dy",
        "jupyter": {
          "outputs_hidden": false
        },
        "outputId": "d2495b7b-3748-47ae-86c5-5480c490bace"
      },
      "source": [
        "print(model_path)\n",
        "print(\"anchors:\",anchors)\n",
        "print(\"labels:\",labels)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "./train/k210_work/k210/ubuntu/out/yolo_2021-09-27_06_10/m.h5\n",
            "anchors: [4.25, 4.09375, 2.4375, 2.40625, 5.15625, 5.15625, 1.6875, 1.78125, 3.28125, 3.28125]\n",
            "labels: ['0', '1']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r0PjjlCpR8Dy"
      },
      "source": [
        "## 生成k210专用模型 kmodel"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "zYzjaJpoR8Dy",
        "jupyter": {
          "outputs_hidden": false
        },
        "outputId": "d0469c20-ffae-4ad3-86ff-c42d9b202108"
      },
      "source": [
        "'''\n",
        "上传 ncc文件\n",
        "下载：https://github.com/kendryte/nncase/releases/tag/v0.1.0-rc5\n",
        "\n",
        "unzip -o -d /home/aistudio/work/ /home/aistudio/work/ncc-linux-x86_64.zip\n",
        "目录为：\n",
        "~/work$ ls\n",
        "ncc-linux-x86_64  ncc-linux-x86_64.zip\n",
        "'''"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\n上传 ncc文件\\n下载：https://github.com/kendryte/nncase/releases/tag/v0.1.0-rc5\\n\\nunzip -o -d /home/aistudio/work/ /home/aistudio/work/ncc-linux-x86_64.zip\\n目录为：\\n~/work$ ls\\nncc-linux-x86_64  ncc-linux-x86_64.zip\\n'"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_nUE0UJ0YA4-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ef1b74f8-d630-46e5-a66d-cb1120a2b6b4"
      },
      "source": [
        "import os\n",
        "# 只处理1次\n",
        "cmd2 = f\"unzip -o -d {work_dir}/ncc01/ {work_dir}/ncc-linux-x86_64.zip\"\n",
        "f=os.popen(cmd2)\n",
        "print(f.read())"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  ./train/k210_work/ncc-linux-x86_64.zip\n",
            "   creating: ./train/k210_work/ncc01/ncc-linux-x86_64/\n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/._ncc-linux-x86_64  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/Microsoft.CSharp.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/._Microsoft.CSharp.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/System.Web.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/._System.Web.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/System.Diagnostics.Contracts.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/._System.Diagnostics.Contracts.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/System.ServiceModel.Web.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/._System.ServiceModel.Web.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/System.Diagnostics.FileVersionInfo.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/._System.Diagnostics.FileVersionInfo.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/System.Runtime.Intrinsics.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/._System.Runtime.Intrinsics.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/System.Net.Security.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/._System.Net.Security.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/NnCase.Converter.K210.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/._NnCase.Converter.K210.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/System.Xml.XPath.XDocument.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/._System.Xml.XPath.XDocument.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/System.Xml.Linq.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/._System.Xml.Linq.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/System.Net.WebSockets.Client.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/._System.Net.WebSockets.Client.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/libsos.so  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/._libsos.so  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/createdump  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/._createdump  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/System.IO.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/._System.IO.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/Microsoft.DotNet.PlatformAbstractions.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/._Microsoft.DotNet.PlatformAbstractions.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/System.Dynamic.Runtime.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/._System.Dynamic.Runtime.dll  \n",
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            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/refs/System.Reflection.Emit.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/refs/._System.Reflection.Emit.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/refs/System.IO.FileSystem.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/refs/._System.IO.FileSystem.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/refs/System.Runtime.Serialization.Json.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/refs/._System.Runtime.Serialization.Json.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/refs/System.Security.Cryptography.Algorithms.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/refs/._System.Security.Cryptography.Algorithms.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/refs/System.Threading.Thread.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/refs/._System.Threading.Thread.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/refs/System.Numerics.Vectors.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/refs/._System.Numerics.Vectors.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/refs/System.ServiceProcess.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/refs/._System.ServiceProcess.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/refs/System.Linq.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/refs/._System.Linq.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/refs/System.Diagnostics.Process.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/refs/._System.Diagnostics.Process.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/refs/System.Numerics.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/refs/._System.Numerics.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/refs/System.Xml.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/refs/._System.Xml.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/refs/System.Diagnostics.Tools.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/refs/._System.Diagnostics.Tools.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/refs/System.Collections.Immutable.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/refs/._System.Collections.Immutable.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/refs/System.Diagnostics.Debug.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/refs/._System.Diagnostics.Debug.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/refs/Microsoft.Win32.Primitives.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/refs/._Microsoft.Win32.Primitives.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/refs/System.Runtime.Serialization.Primitives.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/refs/._System.Runtime.Serialization.Primitives.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/refs/System.Transactions.Local.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/refs/._System.Transactions.Local.dll  \n",
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            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/refs/._System.Drawing.Primitives.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/refs/System.Drawing.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/refs/._System.Drawing.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/refs/System.Runtime.Serialization.Formatters.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/refs/._System.Runtime.Serialization.Formatters.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/refs/System.Net.Ping.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/refs/._System.Net.Ping.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/refs/System.Diagnostics.StackTrace.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/refs/._System.Diagnostics.StackTrace.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/refs/System.Security.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/refs/._System.Security.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/refs/System.Reflection.DispatchProxy.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/refs/._System.Reflection.DispatchProxy.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/refs/System.Core.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/refs/._System.Core.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/refs/System.Net.WebClient.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/refs/._System.Net.WebClient.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/refs/System.ComponentModel.DataAnnotations.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/refs/._System.ComponentModel.DataAnnotations.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/refs/System.Memory.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/refs/._System.Memory.dll  \n",
            "  inflating: ./train/k210_work/ncc01/ncc-linux-x86_64/refs/System.Net.Primitives.dll  \n",
            "  inflating: ./train/k210_work/ncc01/__MACOSX/ncc-linux-x86_64/refs/._System.Net.Primitives.dll  \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8k7ssLloR8Dy",
        "jupyter": {
          "outputs_hidden": false
        },
        "outputId": "4212d456-5af5-4d05-c359-2f6339835cf9"
      },
      "source": [
        "import os\n",
        "# 有时无法执行，why?不可以公用！\n",
        "\n",
        "ncc_path = f\"{work_dir}/ncc01/ncc-linux-x86_64/ncc\" # ncc\n",
        "cmd_linux5 = f\"{ncc_path} -i tflite -o k210model --dataset {temp_saves_dir}sample_images  {temp_saves_dir}m.tflite  {temp_saves_dir}result/m.kmodel\"\n",
        "print(cmd_linux5)\n",
        "\n",
        "# -i tflite -o k210model --dataset ./$1/sample_images ./$1/m.tflite ./$1/result/m.kmodel"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "./train/k210_work/ncc01/ncc-linux-x86_64/ncc -i tflite -o k210model --dataset ./train/k210_work/k210/ubuntu/out/yolo_2021-09-27_06_10/sample_images  ./train/k210_work/k210/ubuntu/out/yolo_2021-09-27_06_10/m.tflite  ./train/k210_work/k210/ubuntu/out/yolo_2021-09-27_06_10/result/m.kmodel\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E1GP2MCbArmY"
      },
      "source": [
        "# %%bash\n",
        "# ./train/k210_work/ncc01/ncc-linux-x86_64/ncc "
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4hjKOCe1R8Dz",
        "jupyter": {
          "outputs_hidden": false
        },
        "outputId": "4163b970-3663-41e2-d13b-902b28285657"
      },
      "source": [
        "f2=os.popen(cmd_linux5)\n",
        "print(f2.read())"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0: InputLayer -> 1x3x224x224\n",
            "1: K210Conv2d 1x3x224x224 -> 1x24x112x112\n",
            "2: K210Conv2d 1x24x112x112 -> 1x24x112x112\n",
            "3: K210Conv2d 1x24x112x112 -> 1x48x112x112\n",
            "4: K210Conv2d 1x48x112x112 -> 1x48x112x112\n",
            "5: K210Conv2d 1x48x112x112 -> 1x96x56x56\n",
            "6: K210Conv2d 1x96x56x56 -> 1x96x56x56\n",
            "7: K210Conv2d 1x96x56x56 -> 1x96x56x56\n",
            "8: K210Conv2d 1x96x56x56 -> 1x96x56x56\n",
            "9: K210Conv2d 1x96x56x56 -> 1x192x28x28\n",
            "10: K210Conv2d 1x192x28x28 -> 1x192x28x28\n",
            "11: K210Conv2d 1x192x28x28 -> 1x192x28x28\n",
            "12: K210Conv2d 1x192x28x28 -> 1x192x28x28\n",
            "13: K210Conv2d 1x192x28x28 -> 1x384x14x14\n",
            "14: K210Conv2d 1x384x14x14 -> 1x384x14x14\n",
            "15: K210Conv2d 1x384x14x14 -> 1x384x14x14\n",
            "16: K210Conv2d 1x384x14x14 -> 1x384x14x14\n",
            "17: K210Conv2d 1x384x14x14 -> 1x384x14x14\n",
            "18: K210Conv2d 1x384x14x14 -> 1x384x14x14\n",
            "19: K210Conv2d 1x384x14x14 -> 1x384x14x14\n",
            "20: K210Conv2d 1x384x14x14 -> 1x384x14x14\n",
            "21: K210Conv2d 1x384x14x14 -> 1x384x14x14\n",
            "22: K210Conv2d 1x384x14x14 -> 1x384x14x14\n",
            "23: K210Conv2d 1x384x14x14 -> 1x384x14x14\n",
            "24: K210Conv2d 1x384x14x14 -> 1x384x14x14\n",
            "25: K210Conv2d 1x384x14x14 -> 1x768x7x7\n",
            "26: K210Conv2d 1x768x7x7 -> 1x768x7x7\n",
            "27: K210Conv2d 1x768x7x7 -> 1x768x7x7\n",
            "28: K210Conv2d 1x768x7x7 -> 1x35x7x7\n",
            "29: Dequantize 1x35x7x7 -> 1x35x7x7\n",
            "30: OutputLayer 1x35x7x7\n",
            "KPU memory usage: 2097152 B\n",
            "Main memory usage: 8584 B\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rj5xcxFHR8Dz",
        "jupyter": {
          "outputs_hidden": false
        },
        "outputId": "5b3847b8-a315-490d-f48a-691a2f374fd1"
      },
      "source": [
        "# 生成的文件\n",
        "cmd = f\"ls {temp_saves_dir}result\"\n",
        "print(\"cmd:\",cmd)\n",
        "f=os.popen(cmd)\n",
        "print(f.read())"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cmd: ls ./train/k210_work/k210/ubuntu/out/yolo_2021-09-27_06_10/result\n",
            "boot.py\n",
            "m.kmodel\n",
            "report.jpg\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ndaYzQt-R8Dz"
      },
      "source": [
        "## 生成k210程序boot.py"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4c-xSzJ1R8D0",
        "jupyter": {
          "outputs_hidden": false
        },
        "outputId": "70721341-e183-4ca7-ac34-dcb5c76c88f1"
      },
      "source": [
        "target_file = f\"{temp_saves_dir}result/boot.py\"\n",
        "copy_file1 = f\"cp -f {git_dir}/k210_train/data/template/yolo_boot.py {target_file}\"\n",
        "print(copy_file1)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cp -f ./train/k210_work/k210/k210_train/data/template/yolo_boot.py ./train/k210_work/k210/ubuntu/out/yolo_2021-09-27_06_10/result/boot.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AnfUkLauR8D0",
        "jupyter": {
          "outputs_hidden": false
        },
        "outputId": "6db5430d-1571-4c77-d417-4b7f13cf0096"
      },
      "source": [
        "f1=os.popen(copy_file1)\n",
        "print(f1.read())"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hY2dLO-_R8D0",
        "jupyter": {
          "outputs_hidden": false
        }
      },
      "source": [
        "# 更新 labels \n",
        "result_boot_py_path = target_file\n",
        "\n",
        "img_size = [224,224]\n",
        "#labels = [\"right\", \"left\", \"forward\"]\n",
        "replace = 'labels = [\"{}\"]'.format('\", \"'.join(labels))\n",
        "\n",
        "with open(result_boot_py_path) as f:\n",
        "            boot_py = f.read()\n",
        "with open(result_boot_py_path, \"w\") as f:\n",
        "            target = 'labels = [\"0\", \"1\"] # labels'\n",
        "            boot_py = boot_py.replace(target, replace)\n",
        "            target = 'anchors = [3.28125, 3.28125] # anchors'\n",
        "            replace = 'anchors = [{}]'.format(', '.join(str(i) for i in anchors))\n",
        "            target = 'img_size = [224,224] # img_size'\n",
        "            replace = 'img_size = [{},{}]'.format(img_size[0], img_size[1])\n",
        "            boot_py = boot_py.replace(target, replace)\n",
        "            f.write(boot_py)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_4hEFujRR8D0"
      },
      "source": [
        "## 更新到k210 的cube硬件上运行"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x-E08XApR8D0",
        "jupyter": {
          "outputs_hidden": false
        },
        "outputId": "44be8a99-fc6a-4e33-e884-6890e0245343"
      },
      "source": [
        "# 生成的文件\n",
        "cmd = f\"ls {temp_saves_dir}result\"\n",
        "print(\"cmd:\",cmd)\n",
        "f=os.popen(cmd)\n",
        "print(f.read())"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cmd: ls ./train/k210_work/k210/ubuntu/out/yolo_2021-09-27_06_10/result\n",
            "boot.py\n",
            "report.jpg\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "r9FbrXAcR8D1",
        "jupyter": {
          "outputs_hidden": false
        },
        "outputId": "9a192749-6fa9-487a-a369-b61d66889d4e"
      },
      "source": [
        "'''\n",
        "把 \n",
        "├── result\n",
        "│   ├── boot.py\n",
        "│   └── m.kmodel\n",
        "\n",
        "的 boot.py ，m.kmodel 复制到 sd 卡的根目录下（或者 models/m8/目录下）\n",
        "重新启动硬件，就可以运行了\n",
        "boot.py 代码中核实模型的路径是否正确\n",
        "'''"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\n把 \\n├── result\\n│\\xa0\\xa0 ├── boot.py\\n│\\xa0\\xa0 └── m.kmodel\\n\\n的 boot.py ，m.kmodel 复制到 sd 卡的根目录下（或者 models/m8/目录下）\\n重新启动硬件，就可以运行了\\nboot.py 代码中核实模型的路径是否正确\\n'"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    }
  ]
}